A number of previous works [1]}, [2]}, [3]} have investigated similar motif-based molecule generation based on the variational autoencoders (VAE). JT-VAE and HierVAE [1]}, [2]} decompose and reconstruct molecules into a tree structure of motifs. These models might not be compatible with the scaffold-based generation, since a latent vector from their encoders depends on a motif-wise decomposition order which is irrelevant information for docking score that may bias the subsequent generation [3]}. Maziarz et al. [3]} also propose a VAE-based model which encodes a given scaffold with graph neural networks (GNNs) and decodes the motif-adding actions to produce an extended molecule. While Maziarz et al.'s motif-adding actions resemble our generation steps, we additionally introduce connectivity-preserving fragmentation and augmentation procedure which helps our model generate molecules of better quality.
